Machine Learning: An In-Depth Guide – Data Selection, Preparation, and Modeling

Welcome to the second article in a five-part series about machine learning. In this article, we will briefly introduce model performance concepts, and then focus on the following parts of the machine learning process: data selection, preprocessing, feature selection, model selection, and model tradeoff considerations. Model performance can be defined in many ways, but in general, it refers to how effectively the model is able to achieve the solution goals for a given problem (e.g., prediction, classification, anomaly detection, recommendation). Since the goals can differ for each problem, the measure of performance can differ as well. Some common performance measures include accuracy, precision, recall, receiver operator characteristic (ROC), and so on.